title
Power BI Project For Beginners | Sales Insights Data Analysis Project - 4 - Data Cleaning & ETL
description
In this video, we will plug mysql database with Power BI. In power BI we will do data cleaning and ETL (Extract, transform, load). This process is also known as data munging or data wrangling. We will do currency normalization, handle invalid values, etc.
To download all the resource files: https://codebasics.io/resources/sales-insights-data-analysis-project
(You can find the SQL queries, setup instructions, and Power BI formula in the Readme file)
Previous video: https://www.youtube.com/watch?v=JOrhcV3_NAk&list=PLeo1K3hjS3utcb9nKtanhcn8jd2E0Hp9b&index=3
Next video: https://www.youtube.com/watch?v=pbOJVEsZKJ8&list=PLeo1K3hjS3utcb9nKtanhcn8jd2E0Hp9b&index=5
How to learn data analyst skills for free: https://youtu.be/x6tnVOn4st4
Machine learning tutorial playlist: https://www.youtube.com/watch?v=gmvvaobm7eQ&list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw
Entire playlist for this project: https://www.youtube.com/playlist?list=PLeo1K3hjS3uva8pk1FI3iK9kCOKQdz1I9
Special thanks to my friend, Hemanand Vadivel (https://www.linkedin.com/in/hemanand-vadivel-0b34aab5/) who is an experienced data analyst manager working for a company in UK. He has a major contribution in this project.
Basics of DAX & Data Modelling are the two fundamental technical skills required to author Power BI reports – There are the free courses I recommend from SQLBI.com
DAX: https://www.sqlbi.com/p/introducing-dax-video-course/
Data Modelling: https://www.sqlbi.com/p/introduction-to-data-modeling-for-power-bi-video-course/
#️⃣ Social Media #️⃣
🔗 Discord: https://discord.gg/r42Kbuk
📸 Dhaval's Personal Instagram: https://www.instagram.com/dhavalsays/
📸 Instagram: https://www.instagram.com/codebasicshub/
🔊 Facebook: https://www.facebook.com/codebasicshub
📝 Linkedin (Personal): https://www.linkedin.com/in/dhavalsays/
📝 Linkedin (Codebasics): https://www.linkedin.com/company/codebasics/
📱 Twitter: https://twitter.com/codebasicshub
🔗 Patreon: https://www.patreon.com/codebasics?fan_landing=true
detail
{'title': 'Power BI Project For Beginners | Sales Insights Data Analysis Project - 4 - Data Cleaning & ETL', 'heatmap': [{'end': 207.095, 'start': 146.017, 'weight': 0.843}, {'end': 590.657, 'start': 559.921, 'weight': 1}, {'end': 1264.106, 'start': 1245.155, 'weight': 0.735}], 'summary': 'Covers using power bi to connect with a mysql database, perform etl processes, build a data model, and establish data model relationships. it also demonstrates data transformation using power query, filtering sales transactions, and currency conversion from usd to inr.', 'chapters': [{'end': 200.751, 'segs': [{'end': 84.704, 'src': 'embed', 'start': 56.398, 'weight': 0, 'content': [{'end': 60.74, 'text': "But if you don't have this installed, you will see install button here.", 'start': 56.398, 'duration': 4.342}, {'end': 67.443, 'text': 'OK, you can also launch power BI by going here and just typing it power BI.', 'start': 61.18, 'duration': 6.263}, {'end': 69.564, 'text': 'So see power BI desktop.', 'start': 68.263, 'duration': 1.301}, {'end': 72.385, 'text': 'This is the option you want to install.', 'start': 69.604, 'duration': 2.781}, {'end': 76.988, 'text': "Now what we'll do in today's tutorial is we will.", 'start': 73.706, 'duration': 3.282}, {'end': 84.704, 'text': 'Pull this data in Power BI will do our transformation or ETL and then we will build a data model.', 'start': 77.981, 'duration': 6.723}], 'summary': 'Tutorial on installing power bi, pulling data, etl, and building a data model.', 'duration': 28.306, 'max_score': 56.398, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6pifKxjyHd8/pics/6pifKxjyHd856398.jpg'}], 'start': 1.107, 'title': 'Data analysis and data connection with power bi', 'summary': 'Covers using power bi to connect with a mysql database, perform etl processes, build a data model, and install power bi on a windows machine. it also explains the process of connecting power bi to a mysql database and different data sources such as excel, csv, and json files.', 'chapters': [{'end': 112.759, 'start': 1.107, 'title': 'Data analysis with power bi', 'summary': 'Discusses using power bi to connect with a mysql database, perform etl process for data cleaning and transformation, and build a suitable data model for analysis; it also outlines the steps to install power bi on a windows machine.', 'duration': 111.652, 'highlights': ['The chapter discusses using Power BI to connect with a MySQL database and perform ETL process for data cleaning and transformation.', 'The chapter outlines the steps to install Power BI on a Windows machine.']}, {'end': 200.751, 'start': 112.759, 'title': 'Power bi data connection', 'summary': 'Explains how to connect power bi to a mysql database, demonstrating step-by-step instructions and highlighting the ease of connecting different data sources, including excel, csv, and json files.', 'duration': 87.992, 'highlights': ['The Power BI UI allows users to seamlessly connect to various data sources, including Excel, CSV, JSON files, and different databases, such as MySQL, simplifying the data importing process.', 'Step-by-step instructions are provided for connecting Power BI to a MySQL database, including specifying the server location and database name, with default username and password settings explained, ensuring a smooth connection process.']}], 'duration': 199.644, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6pifKxjyHd8/pics/6pifKxjyHd81107.jpg', 'highlights': ['The Power BI UI allows seamless connection to various data sources, simplifying data importing process.', 'The chapter discusses using Power BI to connect with a MySQL database and perform ETL process for data cleaning and transformation.', 'Step-by-step instructions are provided for connecting Power BI to a MySQL database, ensuring a smooth connection process.', 'The chapter outlines the steps to install Power BI on a Windows machine.']}, {'end': 546.838, 'segs': [{'end': 265.873, 'src': 'embed', 'start': 232.632, 'weight': 3, 'content': [{'end': 245.394, 'text': "So what's happening now? is Power BI is connecting with MySQL, pulling all the records from these five tables into Power BI environment.", 'start': 232.632, 'duration': 12.762}, {'end': 253.138, 'text': 'And once this is here, we will use Power Query.', 'start': 246.575, 'duration': 6.563}, {'end': 258.519, 'text': 'Power Query is a tool that we can use to transform our data.', 'start': 253.798, 'duration': 4.721}, {'end': 261.41, 'text': "So let's see.", 'start': 260.548, 'duration': 0.862}, {'end': 265.873, 'text': 'So on the Power BI left-hand side, there are three options.', 'start': 262.21, 'duration': 3.663}], 'summary': 'Power bi connects to mysql, imports records from five tables, and uses power query for data transformation.', 'duration': 33.241, 'max_score': 232.632, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6pifKxjyHd8/pics/6pifKxjyHd8232632.jpg'}, {'end': 378.599, 'src': 'embed', 'start': 350.086, 'weight': 2, 'content': [{'end': 363.508, 'text': 'similarly, in products, in products table there is product underscore code which is same as product underscore code in sales transaction.', 'start': 350.086, 'duration': 13.422}, {'end': 369.092, 'text': 'so the name of these fields are similar product underscore code, product underscore code.', 'start': 363.508, 'duration': 5.584}, {'end': 373.195, 'text': 'so based on that, it established this relationship.', 'start': 369.092, 'duration': 4.103}, {'end': 378.599, 'text': "now some of these relationships cannot be established, so we'll do it manually somehow.", 'start': 373.195, 'duration': 5.404}], 'summary': 'Established relationships between products and sales transactions based on similar product codes.', 'duration': 28.513, 'max_score': 350.086, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6pifKxjyHd8/pics/6pifKxjyHd8350086.jpg'}, {'end': 463.214, 'src': 'embed', 'start': 403.594, 'weight': 0, 'content': [{'end': 412.698, 'text': 'so it connects both table with that relationship and when you over here it will show you this thing now.', 'start': 403.594, 'duration': 9.104}, {'end': 415.28, 'text': 'this is called a star schema, by the way.', 'start': 412.698, 'duration': 2.582}, {'end': 419.442, 'text': "so if you have heard about star schema, so let's just do google.", 'start': 415.28, 'duration': 4.162}, {'end': 423.144, 'text': 'so what is star schema?', 'start': 419.442, 'duration': 3.702}, {'end': 432.469, 'text': 'so if you search for star schema, this is a concept in the world of data analytics and data warehousing.', 'start': 423.144, 'duration': 9.325}, {'end': 439.936, 'text': 'so if you click on this particular visual, it will show you what is star schema.', 'start': 432.469, 'duration': 7.467}, {'end': 445.28, 'text': 'so, or maybe click here.', 'start': 439.936, 'duration': 5.344}, {'end': 448.422, 'text': 'so see, i will open this image in a new tab.', 'start': 445.28, 'duration': 3.142}, {'end': 450.124, 'text': 'so what this?', 'start': 448.422, 'duration': 1.702}, {'end': 453.947, 'text': 'and thanks to guru99.com for doing this image.', 'start': 450.124, 'duration': 3.823}, {'end': 460.271, 'text': 'so in star schema there is a fact table and there is dimension table in our case.', 'start': 453.947, 'duration': 6.324}, {'end': 463.214, 'text': 'see, the fact table is a transaction.', 'start': 461.232, 'duration': 1.982}], 'summary': 'Introduction to star schema in data analytics and data warehousing, with fact and dimension tables.', 'duration': 59.62, 'max_score': 403.594, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6pifKxjyHd8/pics/6pifKxjyHd8403594.jpg'}], 'start': 201.832, 'title': 'Power bi, data model relationships, and star schema', 'summary': 'Covers power bi connection with mysql, data transformation using power query, and 5 tables for visualization. it also explains establishing data model relationships, creating connections between tables, and the importance of star schema in data analytics and data warehousing.', 'chapters': [{'end': 296.578, 'start': 201.832, 'title': 'Power bi data transformation', 'summary': 'Covers connecting power bi with mysql, loading and transforming data, using power query for data transformation, and building visualization reports with five tables available for visualization.', 'duration': 94.746, 'highlights': ['Power BI connects with MySQL to pull records from five tables into the Power BI environment.', 'Power Query is a tool used for data transformation, and it is utilized to transform the data pulled from MySQL.', 'In Power BI, visualization or BI dashboards are referred to as reports, and there are five tables available for building visualization reports.', 'When connecting with one table, users can directly click on transform data to perform transformation, or they can choose to load all the information first.']}, {'end': 403.594, 'start': 296.598, 'title': 'Understanding data model relationships', 'summary': 'Explains the process of establishing relationships in a data model, demonstrating how to automatically and manually create connections between different tables and fields within a data model.', 'duration': 106.996, 'highlights': ['The data model shows relationships between different tables, with some established automatically and others requiring manual creation, such as linking market code and markets code.', 'Automatic establishment of relationships is based on the similarity of field names, such as customer code in the customer table and sales transaction table.', 'Manual establishment of relationships involves dragging and dropping fields to connect them, such as linking order date in one table to date in another table.']}, {'end': 546.838, 'start': 403.594, 'title': 'Understanding star schema in data analytics', 'summary': 'Explains the concept of star schema in data analytics and data warehousing, which consists of a fact table representing business events and dimension tables such as customer, product, and market, connected in a star-like manner, crucial for data modeling in a data analyst or data engineering role.', 'duration': 143.244, 'highlights': ['The concept of star schema in data analytics and data warehousing, comprising a fact table representing business events and dimension tables, is explained, emphasizing the importance of data modeling for a data analyst or data engineering role.', 'The fact table in a star schema represents actual business transactions and activities, while the dimension tables, such as customer, product, and market, are entities that connect with the fact table, forming a star-like structure, crucial for establishing relationships between data tables.', 'Understanding and implementing star schema is essential for data analysts and data engineers, as it involves creating different tables and establishing relationships between them to solve business problems.']}], 'duration': 345.006, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6pifKxjyHd8/pics/6pifKxjyHd8201832.jpg', 'highlights': ['Power BI connects with MySQL to pull records from five tables into the Power BI environment.', 'Power Query is utilized to transform the data pulled from MySQL for visualization.', 'The data model shows relationships between different tables, with some established automatically and others requiring manual creation.', 'The concept of star schema in data analytics and data warehousing is explained, emphasizing the importance of data modeling for a data analyst or data engineering role.']}, {'end': 793.077, 'segs': [{'end': 624.769, 'src': 'heatmap', 'start': 559.921, 'weight': 0, 'content': [{'end': 569.605, 'text': "because at least hardware is running business only in India right now and New York and Paris happens to be some value that exists in our database and we don't care about those.", 'start': 559.921, 'duration': 9.684}, {'end': 572.866, 'text': 'So for that you can click on transform data.', 'start': 570.385, 'duration': 2.481}, {'end': 577.608, 'text': 'So when you click on transform data, it is going to launch a power query editor.', 'start': 573.306, 'duration': 4.302}, {'end': 587.275, 'text': 'this is the place where you can do your etl or your transformation, or your data cleaning data, munging data wrangling,', 'start': 578.408, 'duration': 8.867}, {'end': 590.657, 'text': 'whatever you fancy term you want to call it.', 'start': 587.275, 'duration': 3.382}, {'end': 595.021, 'text': 'we are transforming data and we are cleaning unnecessary values here.', 'start': 590.657, 'duration': 4.364}, {'end': 605.125, 'text': "that's why this is, uh, call of power, and This is the place basically where you do your transformation, and it's called power query editor.", 'start': 595.021, 'duration': 10.104}, {'end': 607.326, 'text': 'So I will go to markets here.', 'start': 606.065, 'duration': 1.261}, {'end': 612.527, 'text': 'And power BI is nothing but an Excel on steroids.', 'start': 608.506, 'duration': 4.021}, {'end': 615.027, 'text': 'So many things that you can do in Microsoft Excel.', 'start': 612.567, 'duration': 2.46}, {'end': 616.647, 'text': 'You can do same thing here.', 'start': 615.547, 'duration': 1.1}, {'end': 620.268, 'text': 'So here in zone I will say.', 'start': 617.227, 'duration': 3.041}, {'end': 624.769, 'text': 'You know tax free filters and you can say.', 'start': 620.288, 'duration': 4.481}], 'summary': 'Hardware business runs in india, using power query editor for data transformation in power bi.', 'duration': 51.463, 'max_score': 559.921, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6pifKxjyHd8/pics/6pifKxjyHd8559921.jpg'}, {'end': 793.077, 'src': 'embed', 'start': 767.403, 'weight': 5, 'content': [{'end': 776.145, 'text': 'You know in the navigation you see all this data and you see values like this minus one but when you go and filter rows that value is gone.', 'start': 767.403, 'duration': 8.742}, {'end': 786.848, 'text': 'Here also you see this kind of particular formula where it is saying sales amount not equal to minus one and not equal to zero.', 'start': 777.025, 'duration': 9.823}, {'end': 793.077, 'text': 'OK, basically saying filter everything which has minus one or zero.', 'start': 789.736, 'duration': 3.341}], 'summary': 'Filtering sales data to exclude -1 and 0 values.', 'duration': 25.674, 'max_score': 767.403, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6pifKxjyHd8/pics/6pifKxjyHd8767403.jpg'}], 'start': 548.158, 'title': 'Using power query editor in power bi and filtering sales transactions', 'summary': 'Discusses using the power query editor in power bi to filter unnecessary values, such as new york and paris, and filtering problematic sales transaction values, resulting in the removal of specific records with negative or zero sales amounts.', 'chapters': [{'end': 681.458, 'start': 548.158, 'title': 'Using power query editor in power bi', 'summary': 'Discusses using the power query editor in power bi to filter unnecessary values, exemplifying with the removal of new york and paris from the markets table, showcasing the capabilities of power bi in data transformation.', 'duration': 133.3, 'highlights': ['The Power Query Editor in Power BI is used to filter unnecessary values from the markets table, such as removing New York and Paris, demonstrating the capabilities of the tool in data transformation.', 'Power BI is described as an enhanced version of Microsoft Excel, allowing similar functionalities while emphasizing the data transformation capabilities of Power Query Editor.', 'The process of applying the transformation in the Power Query Editor is detailed, showcasing the creation of a formula to filter out specific values like New York and Paris.']}, {'end': 793.077, 'start': 682.379, 'title': 'Filtering sales transactions', 'summary': 'Discusses identifying and filtering out problematic values in the sales transaction table, including identifying and removing records with sales amount equal to minus one, and filtering out zero values, resulting in the removal of two records with sales amount equal to minus one and multiple records with zero sales amount.', 'duration': 110.698, 'highlights': ['The table had two problems: sales amount being minus one and zero values, resulting in the identification of two records with sales amount equal to minus one and multiple records with zero sales amount.', 'Filtering out sales amount equal to minus one and zero resulted in the removal of two records with sales amount equal to minus one and multiple records with zero sales amount.', 'The process of filtering out problematic values was reflected in the step-by-step transformation on the right-hand side of the data pipeline.']}], 'duration': 244.919, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6pifKxjyHd8/pics/6pifKxjyHd8548158.jpg', 'highlights': ['The process of filtering out problematic values was reflected in the step-by-step transformation on the right-hand side of the data pipeline.', 'Filtering out sales amount equal to minus one and zero resulted in the removal of two records with sales amount equal to minus one and multiple records with zero sales amount.', 'The table had two problems: sales amount being minus one and zero values, resulting in the identification of two records with sales amount equal to minus one and multiple records with zero sales amount.', 'The Power Query Editor in Power BI is used to filter unnecessary values from the markets table, such as removing New York and Paris, demonstrating the capabilities of the tool in data transformation.', 'The process of applying the transformation in the Power Query Editor is detailed, showcasing the creation of a formula to filter out specific values like New York and Paris.', 'Power BI is described as an enhanced version of Microsoft Excel, allowing similar functionalities while emphasizing the data transformation capabilities of Power Query Editor.']}, {'end': 1340.919, 'segs': [{'end': 900.833, 'src': 'embed', 'start': 793.317, 'weight': 1, 'content': [{'end': 799.28, 'text': 'OK, so again we did one more data transformation or data cleaning now.', 'start': 793.317, 'duration': 5.963}, {'end': 804.522, 'text': 'Another thing we want to do is we want to convert this USD.', 'start': 800.66, 'duration': 3.862}, {'end': 808.784, 'text': 'These two values into INR because when we do.', 'start': 805.142, 'duration': 3.642}, {'end': 813.366, 'text': 'sum of entire column to get the total revenue.', 'start': 810.024, 'duration': 3.342}, {'end': 817.95, 'text': "I don't want to add 500 into 7176.", 'start': 813.707, 'duration': 4.243}, {'end': 822.052, 'text': 'I want to convert 500 to Indian rupees and then add it.', 'start': 817.95, 'duration': 4.102}, {'end': 828.156, 'text': 'So how do you do that? So for that you have to add a new column.', 'start': 822.553, 'duration': 5.603}, {'end': 837.523, 'text': 'So what I will do is I will create a new column called normalized currency where all the sales amount will be converted into INR.', 'start': 828.557, 'duration': 8.966}, {'end': 839.584, 'text': 'Basically single currency.', 'start': 838.123, 'duration': 1.461}, {'end': 845.288, 'text': 'okay. so for doing that.', 'start': 841.044, 'duration': 4.244}, {'end': 849.232, 'text': 'by the way, i already have the github page that i created.', 'start': 845.288, 'duration': 3.944}, {'end': 854.216, 'text': 'there is a readme file and i already have a formula here.', 'start': 849.232, 'duration': 4.984}, {'end': 856.459, 'text': "so i'm going to use this formula.", 'start': 854.216, 'duration': 2.243}, {'end': 864.152, 'text': "but let's do this thing step by step, okay.", 'start': 856.459, 'duration': 7.693}, {'end': 870.596, 'text': "so i'm gonna go into add column and create a custom column.", 'start': 864.152, 'duration': 6.444}, {'end': 871.777, 'text': 'you can also create a.', 'start': 870.596, 'duration': 1.181}, {'end': 873.919, 'text': "let's see what is conditional column.", 'start': 871.777, 'duration': 2.142}, {'end': 889.727, 'text': "so when you click on conditional column, you can you know, you can say things like Okay, so conditional column is, let's say, currency.", 'start': 873.919, 'duration': 15.808}, {'end': 897.571, 'text': "If currency is equal to let's say USD, then output I want to be one.", 'start': 890.627, 'duration': 6.944}, {'end': 900.833, 'text': "And let's call it custom column.", 'start': 899.512, 'duration': 1.321}], 'summary': 'Data cleaning and currency conversion to inr using a custom column and a formula from a github page.', 'duration': 107.516, 'max_score': 793.317, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6pifKxjyHd8/pics/6pifKxjyHd8793317.jpg'}, {'end': 1125.221, 'src': 'embed', 'start': 1047.059, 'weight': 0, 'content': [{'end': 1048.96, 'text': '75 rupees is equal to 1 dollar.', 'start': 1047.059, 'duration': 1.901}, {'end': 1052.262, 'text': "That's why when I multiply it.", 'start': 1051.021, 'duration': 1.241}, {'end': 1054.323, 'text': 'See I got now my INR.', 'start': 1052.322, 'duration': 2.001}, {'end': 1055.483, 'text': 'Very good.', 'start': 1054.883, 'duration': 0.6}, {'end': 1057.804, 'text': "So in the else I don't want 0.", 'start': 1056.384, 'duration': 1.42}, {'end': 1058.124, 'text': 'I want.', 'start': 1057.804, 'duration': 0.32}, {'end': 1063.222, 'text': 'the Indian rupees.', 'start': 1061.641, 'duration': 1.581}, {'end': 1069.886, 'text': 'perfect, see now, in this column I have all the amounts in Indian currency.', 'start': 1063.222, 'duration': 6.664}, {'end': 1073.368, 'text': 'so this USD is now converted into Indian currency.', 'start': 1069.886, 'duration': 3.482}, {'end': 1074.868, 'text': 'this is similar to the Excel.', 'start': 1073.368, 'duration': 1.5}, {'end': 1079.211, 'text': 'Microsoft Excel formula is just that it has little different syntax.', 'start': 1074.868, 'duration': 4.343}, {'end': 1085.08, 'text': 'so again, to go through our data pipeline, we initially imported our data.', 'start': 1079.211, 'duration': 5.869}, {'end': 1086.201, 'text': 'so you click here.', 'start': 1085.08, 'duration': 1.121}, {'end': 1088.182, 'text': 'you get your raw data here.', 'start': 1086.201, 'duration': 1.981}, {'end': 1090.864, 'text': 'then we filtered the rows, which is minus one.', 'start': 1088.182, 'duration': 2.682}, {'end': 1095.447, 'text': 'so when you click on filter rows, it went away and then we converted.', 'start': 1090.864, 'duration': 4.583}, {'end': 1096.968, 'text': 'we added a new column.', 'start': 1095.447, 'duration': 1.521}, {'end': 1099.269, 'text': 'so this is also known as data transformation.', 'start': 1096.968, 'duration': 2.301}, {'end': 1102.772, 'text': 'you know, etl extract, transform.', 'start': 1099.269, 'duration': 3.503}, {'end': 1109.596, 'text': 'so by transforming we converted usd into indian rupees and load load is basically we are loading data into power.', 'start': 1102.772, 'duration': 6.824}, {'end': 1115.539, 'text': 'bi etl is a terminology used In the world of data warehouse.', 'start': 1109.596, 'duration': 5.943}, {'end': 1119.9, 'text': 'in the one of the, I think, second video or third video, we covered data warehousing concept.', 'start': 1115.539, 'duration': 4.361}, {'end': 1125.221, 'text': 'We are not building a data warehouse here, but you can kind of get an idea on this concept.', 'start': 1119.94, 'duration': 5.281}], 'summary': '75 rupees = 1 dollar. data transformed to indian currency in data pipeline.', 'duration': 78.162, 'max_score': 1047.059, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6pifKxjyHd8/pics/6pifKxjyHd81047059.jpg'}, {'end': 1274.376, 'src': 'heatmap', 'start': 1245.155, 'weight': 0.735, 'content': [{'end': 1254.561, 'text': "so let's do this.", 'start': 1245.155, 'duration': 9.406}, {'end': 1264.106, 'text': 'once you have applied your transformation, which is step one, two, three, what you can do is you can go to home and you can say close and apply.', 'start': 1254.561, 'duration': 9.545}, {'end': 1274.376, 'text': 'so when you do close and apply, it will apply all this transformation and it will take you back to your main power bi dashboard,', 'start': 1264.106, 'duration': 10.27}], 'summary': "After applying the transformation (steps 1-3), use 'close and apply' to apply the changes and return to the main dashboard.", 'duration': 29.221, 'max_score': 1245.155, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6pifKxjyHd8/pics/6pifKxjyHd81245155.jpg'}, {'end': 1305.178, 'src': 'embed', 'start': 1264.106, 'weight': 5, 'content': [{'end': 1274.376, 'text': 'so when you do close and apply, it will apply all this transformation and it will take you back to your main power bi dashboard,', 'start': 1264.106, 'duration': 10.27}, {'end': 1277.539, 'text': 'where you are going to build that visualization.', 'start': 1274.376, 'duration': 3.163}, {'end': 1283.022, 'text': "so if you click here, You don't know now your tables.", 'start': 1277.539, 'duration': 5.483}, {'end': 1285.023, 'text': "so let's say sales transaction.", 'start': 1283.022, 'duration': 2.001}, {'end': 1296.371, 'text': 'So these tables have those transformations applied, OK? So if you look at.', 'start': 1285.624, 'duration': 10.747}, {'end': 1305.178, 'text': 'USD see we have our normalized sales amount here.', 'start': 1301.815, 'duration': 3.363}], 'summary': 'Applying transformations in power bi to build visualizations for sales transactions.', 'duration': 41.072, 'max_score': 1264.106, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6pifKxjyHd8/pics/6pifKxjyHd81264106.jpg'}], 'start': 793.317, 'title': 'Data transformation in power bi', 'summary': 'Covers data currency conversion and normalization from usd to inr, adding custom columns in power bi, and demonstrating data transformation and visualization, emphasizing the importance of learning formula syntax and rectifying currency conversion issues.', 'chapters': [{'end': 864.152, 'start': 793.317, 'title': 'Data currency conversion and normalization', 'summary': 'Involves converting sales amounts from usd to inr to ensure consistent currency and accurate revenue calculations. the process includes creating a new column and using a specific formula outlined in the provided github page.', 'duration': 70.835, 'highlights': ['The process involves converting sales amounts from USD to INR to ensure consistent currency and accurate revenue calculations.', "Creating a new column called 'normalized currency' where all the sales amounts will be converted into INR.", 'Using a specific formula outlined in the provided GitHub page for currency conversion.', 'Ensuring accurate revenue calculation by converting all sales amounts into a single currency.']}, {'end': 1125.221, 'start': 864.152, 'title': 'Adding custom column in power bi', 'summary': 'Explains how to add a custom column in power bi by using conditional and custom columns, including examples of currency conversion and data transformation, emphasizing the importance of learning the formula syntax.', 'duration': 261.069, 'highlights': ['Explaining conditional column and creating custom column in Power BI', 'Demonstrating currency conversion using Power BI formula syntax', 'Emphasizing the importance of learning formula syntax in Power BI']}, {'end': 1340.919, 'start': 1129.022, 'title': 'Data transformation and visualization in power bi', 'summary': 'Demonstrates data transformation and visualization in power bi, including identifying and rectifying currency conversion issues, applying transformations, and building a data model for visualization.', 'duration': 211.897, 'highlights': ['The chapter focuses on identifying and rectifying currency conversion issues in Power BI, specifically addressing the duplication of USD and ensuring proper conversion for accurate visualization.', 'Demonstrates the process of applying transformations in Power BI, emphasizing the steps of applying filters, verifying conversions, and applying transformations for accurate data representation.', 'Explains the process of building a data model in Power BI, highlighting the steps of applying transformations, closing and applying the transformation, and navigating back to the main Power BI dashboard for visualization.', 'Encourages engagement and feedback by requesting viewers to like and share the project, indicating the intention to continue building a Power BI dashboard in the next video.']}], 'duration': 547.602, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6pifKxjyHd8/pics/6pifKxjyHd8793317.jpg', 'highlights': ['Demonstrates the process of applying transformations in Power BI, emphasizing the steps of applying filters, verifying conversions, and applying transformations for accurate data representation.', "Creating a new column called 'normalized currency' where all the sales amounts will be converted into INR.", 'The process involves converting sales amounts from USD to INR to ensure consistent currency and accurate revenue calculations.', 'Explaining conditional column and creating custom column in Power BI', 'Demonstrating currency conversion using Power BI formula syntax', 'Emphasizing the importance of learning formula syntax in Power BI', 'Ensuring accurate revenue calculation by converting all sales amounts into a single currency.', 'The chapter focuses on identifying and rectifying currency conversion issues in Power BI, specifically addressing the duplication of USD and ensuring proper conversion for accurate visualization.', 'Explains the process of building a data model in Power BI, highlighting the steps of applying transformations, closing and applying the transformation, and navigating back to the main Power BI dashboard for visualization.', 'Encourages engagement and feedback by requesting viewers to like and share the project, indicating the intention to continue building a Power BI dashboard in the next video.', 'Using a specific formula outlined in the provided GitHub page for currency conversion.']}], 'highlights': ['Power BI UI simplifies data importing process from various sources.', 'Power BI connects with MySQL for ETL process and data cleaning.', 'Step-by-step instructions for smooth connection to MySQL in Power BI.', 'Demonstrates data transformation using Power Query for visualization.', 'Explains the concept of star schema in data analytics and warehousing.', 'Filtering problematic values in Power Query Editor for data transformation.', 'Demonstrates currency conversion from USD to INR for accurate revenue.', 'Emphasizes the importance of learning formula syntax in Power BI.', 'Building a data model in Power BI for accurate visualization.']}